Li, R.; Wang, X.; Wang, Z.; Zhu, Z.; Liu, Z. Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems. Appl. Sci.2023, 13, 8776.
Li, R.; Wang, X.; Wang, Z.; Zhu, Z.; Liu, Z. Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems. Appl. Sci. 2023, 13, 8776.
Li, R.; Wang, X.; Wang, Z.; Zhu, Z.; Liu, Z. Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems. Appl. Sci.2023, 13, 8776.
Li, R.; Wang, X.; Wang, Z.; Zhu, Z.; Liu, Z. Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems. Appl. Sci. 2023, 13, 8776.
Abstract
The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and failure to find quality issues in a timely manner within manufacturing settings may trigger the propagation of defects downstream. Currently, most of the inspection quality methods are direct measurement followed by manual judgment. The integration of deep learning methods provides a feasible way to identify defects in a timely manner, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model around discrete manufacturing characteristics, and use fuzzy theory to evaluate the quality level of production stages. Building Multivariate Long and short-term memory learning hidden quality representations to extract predictions from multi-level inspection data in manufacturing systems. Finally, by validating the data of actual produced water dispensers according to three evaluation indexes, RMSE, MAE, MAPE, the results show that Multivariate Long and short-term memory has better prediction performance.
Keywords
manufacturing execution system; quality prediction; discrete manufacturing; Multivariate Long and short-term memory
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
